Climate Data RandomForest Analysis
Joey Hulbert
4/8/2022
Purpose
The purpose of this markdown document is to list the steps we followed for refining the models using climate data only.
Data
The data used in the below models are described in the Data Wrangle folder.
Response variables
Note the below analysis uses the iNat data with 1510 observations. Amazing!
- Response variables to consider
- Number of unhealthy trees
- Tree canopy symptoms
Below we compare models using canopy symptoms as the response variable
Explanatory variables
- Explanatory variables included
- iNat data
- explanatory variables such as tree size
- Climate data
- 30yr normals 1991-2020 (265 variables)
- last decade 2011-2020 (265 variables)
- iNat data
Data wrangling
There are multiple methods to group the response variables deepening on desired resolution or fineness of the model.
- Response grouping options
- Original (no change)
- Filtered Symptoms
- We could drop trees outside of main symptom classes
- Note loss of data outside of categories
- We could drop trees outside of main symptom classes
- Reclassified symptoms
- Here we can created an ‘other’ variable to represent trees with the below symptoms
- extra cone crop
- yellowing canopy
- We can also group trees with ‘old dead tops’ and ‘new dead tops’ into a ‘dead top’ category
- Here we can created an ‘other’ variable to represent trees with the below symptoms
- Binary (healthy/unhealthy)
For now, we can move forward with the binary response grouping because it is the broadest and easiest for the model to classify with.
All tree health categories
## # A tibble: 12 × 2
## # Groups: field.tree.canopy.symptoms [12]
## field.tree.canopy.symptoms n
## <fct> <int>
## 1 Branch Dieback or 'Flagging' 74
## 2 Browning Canopy 43
## 3 Candelabra top or very old spike top (old growth) 2
## 4 Extra Cone Crop 4
## 5 Healthy 813
## 6 Multiple Symptoms (please list in Notes) 31
## 7 New Dead Top (red or brown needles still attached) 48
## 8 Old Dead Top (needles already gone) 154
## 9 Other (please describe in Notes) 16
## 10 Thinning Canopy 225
## 11 Tree is dead 74
## 12 Yellowing Canopy 26We also need to filter the data to only include response and explanatory variables we’re interested in. For example, whether a sound clip was included in the iNat data is not important.
We also need to remove other response variables like “field.percent.canopy.affected….” so it is not used as a predictor for tree health.
Note it might be interesting to know if the user was an important factor in predicting if the tree is healthy/unhealthy.
There are also a number of factors that should probably be removed because they may be biasing the data. For example, only trees with the ‘other factor’ question may only be answered for unhealthy trees. We need to think about this a bit more.
Remove variables with variables that have near zero standard deviations (entire column is same value)
Group response variables to binary values
Binary tree health categories
## # A tibble: 2 × 2
## # Groups: field.tree.canopy.symptoms [2]
## field.tree.canopy.symptoms n
## <fct> <int>
## 1 Healthy 813
## 2 Unhealthy 695Approach
- General approach
- build full model using binary response grouping variable
- binary response
- refine model to remove redundant explanatory variables
- build full model using binary response grouping variable
- Approach notes
- We need to train/test models to calculate correctly predicted presences (inverse of commission errors) and correctly predicted absences (inverse of omission errors)
- we may want to evaluate the climate parameters seperately
- we lose >300 observations when we filter out data to only include those with soil
- what happens if we build a model without soils data (without filtering to WA and OR)
Models
- Random forest model criteria
- nearzero variation columns were removed
- Data were imputed prior to model building.
- number of splits was not defined to allow automatic selection,
- ntree = 2001, importance = TRUE, proximity = TRUE, na.action = na.omit
- seed was set for reproducability with: set.seed(71)
Comparing full and reduced datasets of explanatory variables
- Datasets compared below
- Full model = all climate variables retained.
- Monthless model = filtered out the climate parameters for individual months
- Normal only, monthless model = removed decadal data
Full Model
##
## Call:
## randomForest(formula = field.tree.canopy.symptoms ~ ., data = binary, ntree = 2001, importance = TRUE, proximity = TRUE, na.action = na.omit)
## Type of random forest: classification
## Number of trees: 2001
## No. of variables tried at each split: 21
##
## OOB estimate of error rate: 30.7%
## Confusion matrix:
## Healthy Unhealthy class.error
## Healthy 586 227 0.2792128
## Unhealthy 236 459 0.3395683
## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure
Monthless Model
##
## Call:
## randomForest(formula = field.tree.canopy.symptoms ~ ., data = monthless.binary, ntree = 2001, importance = TRUE, proximity = TRUE, na.action = na.omit)
## Type of random forest: classification
## Number of trees: 2001
## No. of variables tried at each split: 12
##
## OOB estimate of error rate: 30.97%
## Confusion matrix:
## Healthy Unhealthy class.error
## Healthy 582 231 0.2841328
## Unhealthy 236 459 0.3395683
Normal, Monthless Model
##
## Call:
## randomForest(formula = field.tree.canopy.symptoms ~ ., data = normal.monthless.binary, ntree = 2001, importance = TRUE, proximity = TRUE, na.action = na.omit)
## Type of random forest: classification
## Number of trees: 2001
## No. of variables tried at each split: 9
##
## OOB estimate of error rate: 30.57%
## Confusion matrix:
## Healthy Unhealthy class.error
## Healthy 584 229 0.2816728
## Unhealthy 232 463 0.3338129
